How Digital Traceability Is Reshaping Food Safety?

 Why Companies Must Adapt Now

The year is 2024, a multistate Listeria outbreak linked to Boar's Head deli meats has sickened 61 people across 19 states, hospitalised 60, and killed 10. The first patient was identified on May 29. The recall was not initiated until July 26, nearly two months later. In that interval, people continued consuming contaminated products, right up until the plant was closed in September. The same year, onions supplied to McDonald's Quarter Pounder hamburgers triggered an E. coli O157:H7 outbreak spanning 14 states, causing more than 100 illnesses, four cases of hemolytic uremic syndrome, and one death. According to data from the U.S. Public Interest Research Group, hospitalizations from foodborne illness in 2024 more than doubled compared to the previous year — from 230 to 487 — and deaths rose from 8 to 19 [1].

 

Neither of these outbreaks was, at its core, a mystery. What they were was slow, where slow to identify the contaminated lot and slow to trace it back through a supply chain that still relies, in significant parts, on paper-based records, incompatible software systems, and manual data entry. Slow to remove product from the market with the precision that modern technology should allow. The CDC's CORE Network data shows that, across 2020–2025, a food vehicle of illness was identified for only 56 percent of outbreak investigations, meaning 44 percent remained unsolved [2]. This is not a new statistic. It is a stubborn one. And it is precisely the number that the global push toward digital traceability is designed to change.

 

The transition is now happening at regulatory speed. The Institute of Food Technologists (IFT) has identified digital tools expanding food safety adoption as one of its top five trends shaping the global food system in 2026 [3]. The Food Marketing Institute (FMI), representing the retail food industry, has named traceability the number one food safety priority for 2026, placing it ahead of produce safety, chemical safety, and sanitation controls [4]. At the regulatory level, the U.S. FDA's Food Safety Modernization Act Rule 204, which was one of the most consequential pieces of food safety regulation in a generation, is rewriting the technical requirements for supply chain data across the entire food industry. Further beyond the United States, the European Union, China, and major trading blocs are simultaneously developing their own digital traceability frameworks, raising fundamental questions about interoperability, harmonization, and what it truly means to build a globally connected, digitally transparent food system.

 

The article examines what digital traceability is, why the industry is being compelled to adopt it now, what the regulatory landscape looks like globally, what technologies are enabling it, what barriers remain, and what food safety professionals and businesses must do in the near term to position themselves on the right side of a transition that is no longer optional.

 

What is Digital Traceability

Before examining the regulatory and technological landscape, it is worth being precise about the term itself, because "traceability" is used loosely in industry contexts in ways that can obscure meaningful distinctions.

 

Traceability, in its regulatory and scientific sense, is the ability to identify and follow the movement of a food product — or a substance intended to be incorporated into a food or feed — through all stages of production, processing, and distribution. The European Union's foundational General Food Law, EC Regulation 178/2002, established it as a legal requirement for all food and feed operators operating in the EU, using what it describes as a "one-step-back, one-step-forward" principle: each operator must be able to identify from whom they received a product and to whom they supplied it [5].

 

What makes digital traceability different from traditional traceability is the nature of the data and the speed at which it can be exchanged, queried, and acted upon. Traditional traceability systems relied on paper-based records, spreadsheets, and proprietary software that managed internal business processes without enabling real-time data exchange between supply chain actors. Digital traceability, by contrast, uses a combination of technologies — barcodes, RFID tags, QR codes, IoT sensors, electronic product code information services (EPCIS), blockchain, and cloud-based platforms — to create a continuous, machine-readable data trail that can be queried by any authorized party in real time [6].

 

The significance of the distinction becomes apparent the moment a recall is needed. A food business that can identify the contaminated lot within minutes and trace every downstream recipient within an hour is operating in a qualitatively different risk environment from one that requires days of manual record-searching. The FDA's vision, articulated in its New Era of Smarter Food Safety Blueprint, is explicit: the goal is faster and more targeted recalls, reduced scope of product removal, fewer illnesses, and ultimately lower costs for both industry and the public [7].

 

The Regulatory Architecture: What Is Being Required, and When

FSMA Rule 204: The United States

The FDA's Food Traceability Final Rule, commonly known as FSMA 204, was published in November 2022 and represents the most substantive expansion of federal traceability requirements since FSMA itself was enacted in 2011. At its core, the rule requires all persons who manufacture, process, pack, or hold foods included on the Food Traceability List (FTL) to maintain records containing Key Data Elements (KDEs) associated with Critical Tracking Events (CTEs), and to be able to provide those records to the FDA within 24 hours upon request [7].

 

The FTL covers a broad range of high-risk commodities: soft and semi-soft cheeses, shell eggs, nut butters, leafy greens, fresh herbs, cucumbers, peppers, tomatoes, sprouts, melons, tropical tree fruits, fresh-cut produce, certain finfish and molluscan shellfish, smoked finfish, crustaceans, and refrigerated ready-to-eat salads [7]. The scope is deliberately wide — these are the categories most frequently implicated in large, multi-state outbreaks, and they represent a substantial share of produce and protein consumption across the United States and for the global exporters who supply the U.S. market.

 

The rule's original compliance deadline of January 20, 2026, has been extended by 30 months to July 20, 2028, following an FDA announcement in March 2025 that acknowledged both the complexity of the rule and the significant technical preparation required across a diverse supply chain [8]. The 30-month extension was subsequently codified by Congress in the Continuing Appropriations Act of 2026. The FDA has been clear that this extension is a window for technical preparation, not a signal that the requirements are being relaxed. The agency hosted a major public stakeholder meeting on lot-level tracking as recently as June 15, 2026, and has actively solicited input on implementation flexibilities while holding firm on the fundamental requirement for digital, lot-level data capture and exchange [9].

 

One consequential development that underscores how the industry is not waiting for federal compliance dates: Walmart's supplier traceability requirements, mandating Advance Shipment Notices with KDE data, SSCC-18 pallet labels, and GS1-128 case labels, which took effect in August 2025, with chargebacks for non-compliant shipments already being assessed [8]. For the large proportion of food manufacturers serving mass retail, the federal compliance date is no longer the operational driver. Their largest customer's requirements already are.

 

The European Union Framework

The EU's approach to food traceability is embedded in a layered legislative architecture, where EC Regulation 178/2002 provides the foundational requirement for traceability across all food and feed operators [5]. On top of the given general framework sits sector-specific regulations for beef and beef labelling, fish and aquaculture products, genetically modified organisms, and organic produce. The EU Food Safety Authority (EFSA) and the German Federal Institute for Risk Assessment (BfR) have been actively developing a Universal Traceability data eXchange (UTX) format and an interoperable multi-actor tracing software ecosystem to support outbreak investigation and rapid alert systems [10].

The EU's Rapid Alert System for Food and Feed (RASFF) is one of the most mature food safety alert networks in the world, and the push toward digital traceability is in part designed to allow RASFF to function at the speed that modern supply chains demand. A 2024 editorial in the Journal of Consumer Protection and Food Safety makes the point directly: food safety authorities worldwide must intensify their efforts to collect and utilize digital traceability data, because as supply chains advance toward Industry 4.0, incorporating IoT sensors and digital twins, the volume of data will grow exponentially and authorities must keep pace [10].

 

The EU Deforestation Regulation (EUDR), which entered into force in 2023 and applies to a range of food commodities including soy, beef, palm oil, and cocoa, adds a further dimension to the EU's traceability requirements: companies must demonstrate that their products are free from deforestation, which in practice requires geolocation data and supply chain documentation down to the production plot level [11]. This represents a significant escalation in what traceability means in the EU context, which is not merely lot-level identification for recall purposes, but verifiable origin data at the level of individual farms and geographic coordinates.

 

China and the Broader Global Context

China has been actively developing national food traceability systems to address domestic food safety concerns and to support export competitiveness. The Chinese government has implemented a series of traceability platforms, including systems specific to pork, dairy, and infant formula, following high-profile food safety scandals that severely damaged consumer trust in domestic producers. A 2025 review in ScienceDirect notes that China's national food traceability architecture is evolving rapidly, though integration across regional systems and harmonization with international data standards remain works in progress [5].

 

The global picture, then, is one of multiple regulatory frameworks converging on a shared direction, “mandatory digital traceability”, while diverging in their specific technical requirements, covered commodities, and timelines. Thus, given divergence has significant practical implications for exporters operating across multiple regulatory environments. A company exporting leafy greens to the United States, fresh fish to the European Union, and dairy products to China must navigate three distinct traceability frameworks simultaneously, and the data formats, identification standards, and information disclosure requirements may differ materially between them.

 

The Technology Layer: What is Enabling Digital Traceability

The technologies underpinning digital traceability form an integrated ecosystem rather than a collection of isolated tools. Understanding how they work together is essential for food businesses making investment and implementation decisions.

 

GS1 Standards: The Universal Language

GS1 is the international, not-for-profit standards organization responsible for the global identification and communication standards that underpin product traceability. Its standards — particularly the Global Trade Item Number (GTIN) for product identification, the Global Location Number (GLN) for location identification, and the SSCC-18 for serialized shipping unit identification — are explicitly recognized by the FDA as a mechanism for meeting the KDE requirements of FSMA 204 [12]. The GS1-128 barcode encodes the GTIN, lot code, expiry date, and quantity on each case, and when combined with GS1's EPCIS (Electronic Product Code Information Services) standard for event data sharing, creates a foundation for interoperable, multi-actor traceability that does not require all parties in a supply chain to use the same software platform [12].

 

The practical significance of GS1 standards is that they represent the closest thing the industry currently has to a universal language for traceability data. A farm that captures harvest data using GS1-compliant identifiers, a processor that records transformation events using EPCIS, a distributor that generates GS1-128 case labels, and a retailer that scans those labels can all exchange traceability information without custom integrations — provided they are using standards-compliant systems. The "provided" clause is doing significant work in that sentence, because adoption of GS1 standards across the full supply chain is still uneven, particularly among smaller operators and producers in low- and middle-income countries [9].

 

Blockchain: Immutability and Multi-Party Trust

Blockchain technology in food traceability has attracted considerable attention since Walmart's landmark 2018 partnership with IBM Food Trust demonstrated that the time required to trace a food item from store to farm could be reduced from approximately seven days to 2.2 seconds using a blockchain-based system. By 2025, a systematic literature review in Business & Information Systems Engineering found that blockchain was the most frequently studied technology for food traceability, appearing in more than 40 percent of selected studies, typically deployed in combination with IoT sensors, RFID tags, or QR codes [13].

 

The fundamental contribution of blockchain to traceability is immutability — once data is entered into a distributed ledger, it cannot be altered retroactively without detection. Such property is valuable in the context of food fraud and in supply chains where multiple parties need to trust each other's records without placing complete confidence in any single actor's database. A 2024 research implementation reported in the blockchain literature showed fraud incident reductions of 80 percent and a rise in fraud detection rates from 70 to 95 percent, with consumer satisfaction index scores rising 12.5 percent [13].

 

However, blockchain's limitations are as important to understand as its benefits. The technology cannot protect against fraud that occurs before data is entered into the system, and the integrity of the physical-digital link depends entirely on the accuracy of the labelling and scanning processes at the point of data capture. As a Frontiers review noted, blockchain integration also requires the combination with IoT sensors and smart tags that automatically collect data, reducing the risk of human error or falsification; without this combination, the immutability of the ledger is only as strong as the honesty of the person entering the data [5]. Cost and scalability remain significant barriers, particularly for smaller operators.

 

IoT and Real-Time Monitoring

Internet of Things sensors: temperature loggers, GPS trackers, RFID readers, and humidity monitors, provide the data capture layer that converts physical events in the supply chain into machine-readable records. Under FSMA 204, the FDA's concept of Critical Tracking Events includes not just growing, receiving, transforming, and shipping, but the conditions under which food is held and transported. IoT sensors can automatically log these conditions in real time, generating continuous data streams that can populate KDE records without manual data entry and trigger alerts when conditions deviate from safe parameters.

 

The FDA's own Low/No-Cost Traceability Challenge, a program designed to identify accessible traceability solutions for smaller operators, recognized both blockchain and IoT as breakthrough solutions precisely because of such automation potential [12]. The cost of IoT sensor hardware has fallen significantly over the past decade, and cloud-based data platforms that aggregate sensor data from multiple supply chain actors are increasingly accessible. For cold chain management specifically, a critical dimension of traceability for fresh produce, seafood, and dairy, where IoT monitoring is rapidly transitioning from a value-added feature to a baseline expectation among major retailers and regulatory bodies.

 

Rapid and Digital Testing Integration

A dimension of digital traceability that is sometimes overlooked is its integration with rapid testing at production and processing points. Next-generation sequencing, rapid immunoassay platforms, and digital PCR systems are generating pathogen detection data in hours rather than days, and the ability to link that testing data directly to lot-level traceability records creates a closed loop between quality control and supply chain documentation. Whole genome sequencing (WGS), already used by FDA and CDC in outbreak investigations to match environmental strains to clinical isolates, is being positioned as the epidemiological backbone of the next generation of foodborne illness surveillance, but its value is amplified when the supply chain records it needs to cross-reference are digital, lot-level, and rapidly accessible [2].

 

The Interoperability Problem: Why Standards Alone Are Not Enough

The single most consequential structural challenge facing digital traceability implementation is interoperability, the ability of different software systems used by different actors in the supply chain to exchange and analyze data accurately and efficiently. The point at which good intentions most frequently collide with operational reality.

 

A 2024 editorial in the Journal of Consumer Protection and Food Safety, authored by Marion Gottschald of the German Federal Institute for Risk Assessment, made this structural problem explicit: while the food industry uses numerous tracing software systems, they are mostly focused on managing internal business processes rather than facilitating data exchange between actors [10]. Food safety authorities have access to some inter-agency tools, including RASFF and FoodChain-Lab, but the widespread adoption of genuinely interoperable software is limited by the lack of available digital traceability data and the limited standardization of tracing data formats across the industry.

 

A 2025 Frontiers systematic review of digitalization in European agri-food supply chains echoed that finding: most studies in the peer-reviewed literature describe conceptual frameworks or pilot implementations rather than fully realized systems, and real-world deployment is hampered by interoperability challenges, scalability issues, regulatory uncertainties, and high costs [14]. The review found that to ensure interoperability across processing and retail stages, harmonized standards, shared APIs, and common data taxonomies are needed, which is a recommendation that has been made many times and implemented unevenly.

 

The practical implication for a food business today is that investing in a traceability system that works internally but cannot communicate with the systems used by suppliers and customers upstream and downstream does not fully deliver on the promise of digital traceability. The value of traceability data is a network effect, which increases with the number of actors who can access, contribute to, and act on it. A manufacturer who has invested in GS1-compliant systems and EPCIS event sharing but whose primary fresh produce supplier is still using paper manifests is operating with a significant gap in their data chain.

 

The FDA stakeholder meeting of June 2026 on lot-level tracking heard exactly the concern from food industry representatives: traceability data today comes in many formats (paper, spreadsheets, and incompatible software systems), which creates both inefficiencies and compliance risks. Participants broadly identified GS1 standards as the most practical common language for global supply chains, while acknowledging that adoption is uneven and that the costs and technical barriers for smaller and less capitalized operators are real [9].

 

TO BE CONTINUED

 

References

[1] U.S. Public Interest Research Group (PIRG) Education Fund. (February 2025). Food for Thought 2025: How safe is our food?. https://pirg.org/edfund/resources/food-for-thought-2025/

[2] Prabhukhot, G. (2026). Regulatory responses to foodborne illness outbreaks in the United States and their implications for food safety. Frontiers in Nutrition, 12, 1717980. https://doi.org/10.3389/fnut.2025.1717980

[3] Niemira, B. (December 2025). What's on the Menu for 2026? IFT's Top Five Food Trends. Institute of Food Technologists. https://www.ift.org/news-and-publications/blog/2025/whats-on-the-menu-for-2026

[4] Eisenbeiser, A. (February 9, 2026). Building the Safest Food System Together: FMI's 2026 Food Safety Priorities. FMI – The Food Industry Association. https://www.fmi.org/blog/view/fmi-blog/2026/02/09/building-the-safest-food-system-together--fmi-s-2026-food-safety-priorities

[5] Reitano, A., et al. (2025). Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends in Food Science & Technology. https://www.sciencedirect.com/science/article/pii/S0924224425002900

[6] Frontiers in Sustainable Food Systems. (April 2026). Food safety and its digital traceability strategies: a supplier-processor profit distribution perspective. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1707114/full

[7] U.S. Food and Drug Administration. FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods (Food Traceability Final Rule). https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods

[8] INECTA. (2026). FSMA 204 Compliance Guide: KDEs, CTEs & July 2028 Deadline. https://www.inecta.com/blog/fsma-204-compliance-guide

[9] OFW Law. (June 23, 2026). FDA's Next Steps on Traceability: Challenges and Solutions in Lot-Level Food Traceability. https://ofwlaw.com/fdas-next-steps-on-traceability-challenges-and-solutions-in-lot-level-food-traceability

[10] Gottschald, M. (2024). Advancing food safety through digital traceability, interoperability, harmonized data and collaborative partnerships. Journal of Consumer Protection and Food Safety, 19, 257–258. https://doi.org/10.1007/s00003-024-01522-8

[11] Natural Trace. (2024). Recent Regulations Driving Traceability in Food and Agriculture Sectors. https://natural-trace.com/recent-regulations-driving-traceability-in-food-and-agriculture-sectors/

[12] GS1 US. Food Safety Modernization Act (FSMA 204): How GS1 Standards Can Help. https://www.supplychain.gs1us.org/standards-and-regulations/food-safety-modernization-act

[13] Vasileiou, K., et al. (2025). Digital Transformation of Food Supply Chain Management Using Blockchain: A Systematic Literature Review Towards Food Safety and Traceability. Business & Information Systems Engineering. https://doi.org/10.1007/s12599-025-00948-0

[14] Frontiers in Blockchain. (October 2025). Digitalization in the European agri-food supply chain: a scoping review of traceability, transparency, and sustainability. https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2025.1701872/full

AI and Food Safety: Can We Trust Machine-Generated Food Advice?

AI vs. Food Safety

The year is 2026, and a home cook in Auckland asks a voice assistant whether leftover rice left at room temperature for eight hours is safe to reheat and eat. The assistant confidently says yes. In Singapore, a food safety manager queries a generative AI chatbot for the regulatory allergen labelling thresholds for sesame in packaged foods, and the model returns a figure that is almost correct, off by a decimal point, and referencing a regulatory version that was superseded two years ago. In both cases, the AI responded fluently, confidently, and incorrectly. Nobody died, but the pattern is concerning, and in food safety, patterns like this eventually produce outcomes that matter very much. Thus, artificial intelligence is reshaping food safety communication, but also introducing new and poorly understood risks.

 

This is not a theoretical argument against artificial intelligence. AI is already delivering real and verifiable benefits across the food safety domain — from pathogen detection in laboratory settings to predictive import surveillance at border controls. The Food and Agriculture Organization of the United Nations (FAO), in its landmark 2025 technical publication developed jointly with Wageningen Food Safety Research, reviewed 141 scientific papers and documented practical AI deployments across inspection, surveillance, border control prioritisation, regulatory efficiency, and risk communication[1]. The report positions AI as a present-day tool, not a future aspiration. That is important context. AI in food safety is not hype — it is happening, and in many cases, it is working.

 

The Communication Gap

Traditional food safety communication had a relatively clear architecture. Regulatory bodies published guidelines. Industry implemented them. Accredited laboratories verified. Certified professionals interpreted the results. Consumers received simplified messages through labelling, public health campaigns, and their general practitioners. The chain was imperfect, but accountability was traceable, which is the communication gap nobody planned for. When something went wrong, there was usually a responsible party identifiable within the system.

 

Generative AI and voice assistants are disrupting this architecture in ways the food safety community has not yet fully reckoned with. Increasingly, both consumers and food industry professionals are bypassing traditional information channels and asking AI systems for food safety guidance directly. This is not a marginal behaviour. According to data from multiple technology research sources, AI-generated search summaries now appear at the top of results for a significant proportion of food-related queries in major markets, and voice assistants handle tens of millions of food-related questions per day globally.

The International Association for Food Protection (IAFP) 2025 Annual Meeting dedicated a full symposium to cutting through the hype of AI in food safety, and the concerns raised by speakers from Chick-fil-A, Ecolab, and the FDA were instructive[2]. David Monk of Chick-fil-A explicitly warned about hallucinations in large language models, the phenomenon where AI models generate plausible but factually fabricated information, and stressed the irreplaceable need for human oversight. Amani Babekir of Ecolab reinforced this directly: AI, she noted, will not eliminate the need for subject matter experts[2]. These are practitioners speaking from real deployment experience, not theoretical concerns.

 

The 2025 FAO report made the same point with institutional weight, explicitly identifying AI hallucinations, where models generate plausible but fabricated information, as a core risk, and warning that premature use of AI in food safety, whether by applying unsuitable techniques or implementing AI without the expertise to interpret its outputs, risks undermining the trust and credibility of the organisations employing it[1].

 

Root Causes

To address the problem properly, it is necessary to understand why it exists rather than simply cataloguing its symptoms. The root causes are structural, and they operate at multiple levels simultaneously.

 

Training Data Quality and Recency

Large language models and generative AI systems are trained on datasets assembled from the internet, scientific publications, regulatory documents, and other text sources. Food safety regulation is a domain characterised by frequent revision. Codex Alimentarius updates maximum residue limits. National regulatory bodies revise allergen thresholds. Recall databases are updated in real time. An AI model trained on data from eighteen months ago may confidently provide guidance based on superseded standards, and the model itself has no awareness of such a limitation, where it does not know what it does not know. The International AI Safety Report 2026 noted explicitly that AI systems can generate non-existent citations, biographies, or facts due to the hallucination phenomenon, with confidence indistinguishable from accurate information[3].

 

Confidence Calibration and the Absence of Uncertainty Signals

Human food safety experts communicate uncertainty. A microbiologist asked about the safety of a novel fermentation process, will hedge, qualify, and direct the questioner to primary sources. Generative AI systems are optimised, in many deployment contexts, to produce fluent and complete-sounding responses. The very qualities that make them engaging as interfaces, conversational fluency, apparent confidence, and absence of hesitation, are precisely the qualities that make them dangerous in high-stakes informational contexts. A consumer asking whether their food is safe to eat needs not just an answer, but an appropriately calibrated signal about how certain that answer is, where current consumer-facing AI systems are structurally poor at delivering it.

 

Regulatory Fragmentation and Jurisdictional Ambiguity

Food safety regulation is deeply jurisdictional, where the maximum level for aflatoxin B1 in cereals intended for direct human consumption is 2 μg/kg in the European Union and 20 μg/kg in the United States, for example, thus a tenfold difference that reflects different risk assessment methodologies and policy choices, not a factual disagreement about toxicology. An AI system that does not know the user's jurisdiction, or that defaults to one regulatory context when the user is operating in another, can deliver technically accurate information for the wrong regulatory environment. In a world where food businesses increasingly operate across multiple jurisdictions, and consumers travel internationally, this is not a minor edge case.

 

Biased and Unrepresentative Training Corpora

A further structural problem is that the training data for general-purpose AI models is heavily skewed toward high-resource, English-language, Western regulatory contexts. Food safety guidance for ASEAN markets, African regulatory frameworks, or small island developing states is systematically underrepresented. A food safety manager in Indonesia, Ghana, or Samoa who queries an AI system in English is likely to receive responses calibrated to FDA or EFSA standards, which may be entirely inapplicable to their regulatory environment and local food production context. The 2025 FAO report noted that data gaps are particularly pronounced for low- and middle-income countries[1], and such gaps translate directly into AI advice that is geographically and contextually unreliable.

 

Accountability Gaps in the Information Chain

Traditional food safety communication is embedded in accountability structures. A food safety consultant who provides incorrect advice can face professional and legal consequences. A regulatory body that publishes incorrect guidance is accountable to its mandate and subject to legislative oversight. An AI system that provides incorrect food safety advice sits outside virtually all of these accountability frameworks, because there is no licensing body for AI food safety advisors, and there is no professional indemnity requirement either. Hence, there is no systematic post-market surveillance of AI-generated food safety information analogous to the adverse event reporting systems that govern medical devices and pharmaceuticals, where such an accountability gap is not a minor regulatory oversight, which is a structural vulnerability that the food safety governance community has barely begun to address.

 

Real Power of AI

A critical analysis must acknowledge genuine achievement alongside genuine risk, and AI in food safety has genuine achievements worth examining carefully, because they also illuminate where the risks concentrate.

 

The FDA has deployed a boosted-tree machine learning model, which is specifically LightGBM, to predict the probability that an imported food shipment will violate regulatory requirements. By combining data on shipment history, product characteristics, and exporting establishment and country risk indicators, the model improves targeting efficiency and increases the likelihood of intercepting unsafe products at the border. This is a well-designed application of AI: it operates within a domain where the training data is well-defined, the outcome is measurable, the model's predictions are reviewed by human inspectors before action is taken, and the consequences of error are caught by subsequent verification steps rather than transmitted directly to end users.

 

Similarly, AI-enabled computer vision systems in food manufacturing environments, by detecting contaminants, verifying packaging integrity, and monitoring temperature compliance in real time, represent applications where AI augments human inspection capacity in controlled, verifiable, high-frequency tasks. The model's outputs are checked against physical reality continuously, errors are corrected in the production flow, and the system operates under the supervision of qualified food safety professionals.

 

In food safety more broadly, AI enables predictive risk modelling, rapid contaminant detection, smart surveillance systems, and blockchain-based traceability, all within contexts where expert human oversight is embedded in the workflow. The pattern that distinguishes good AI deployment from risky AI deployment in food safety is clear: human expertise in the loop, measurable and verifiable outcomes, appropriate uncertainty communication, and domain-specific training data of known quality.

 

The risk concentrates precisely where these conditions are absent, and consumer-facing AI communication, where the advice goes directly to an end user without expert intermediation, is the domain where most of these conditions are missing.

 

Mitigation Strategies

The answer to the question "Can we trust machine-generated food advice?" is not a binary yes or no, which is a conditional answer that depends on context, deployment design, governance, and user literacy. The following mitigation strategies reflect the current state of knowledge and are graded by feasibility and urgency.

 

Domain-Specific AI Systems with Curated, Versioned Knowledge Bases

The most direct technical mitigation is to develop food safety AI applications that do not rely on general-purpose large language models trained on undifferentiated internet data, but instead use curated, jurisdictionally specific, version-controlled knowledge bases. Regulatory databases, Codex Alimentarius texts, and national food standards can be structured as retrieval-augmented generation (RAG) systems, where the AI's outputs are anchored to specific, dated regulatory documents rather than statistical generalisations from training data. This architecture allows the system to say "this answer is based on EU Regulation 2023/XXX, which was current as of this date" rather than generating a confident response from poorly attributed training data. Such systems are technically feasible now, and several national food safety authorities are beginning to pilot them.

 

Mandatory Uncertainty Communication and Source Attribution

AI systems deployed in food safety communication contexts should be required to communicate uncertainty explicitly and to attribute their responses to specific sources. Thus, the choice is not merely a technical design choice, but it should be a regulatory requirement for any AI system that provides food safety guidance in a commercial or public health context. The analogy is nutritional labelling, where it must be such that the food manufacturers are required to declare what is in their product; AI food safety systems should be required to declare the basis and confidence level of their recommendations. The 2026 International AI Safety Report's documentation of AI hallucination risks provides the public health rationale for making this a regulatory rather than voluntary standard[3].

 

Regulatory Frameworks for AI-Generated Food Safety Information

There is currently no coherent international regulatory framework governing AI-generated food safety advice, which is a major gap that Codex Alimentarius, the FAO, and national food safety authorities need to address with some urgency. The framework does not need to be restrictive, but it needs to be clear. At minimum, it should establish that AI systems providing food safety guidance must meet defined accuracy standards, disclose their training data provenance and recency, provide source attribution, communicate uncertainty, and be subject to post-market surveillance for accuracy. The EU AI Act's risk-based classification framework provides one possible model, though its application to food safety communication specifically remains underdeveloped. The International AI Safety Report 2026 notes the importance of expert human oversight as a mitigation for AI hallucination risks[3], and regulatory frameworks should embed this requirement structurally.

 

Organisational AI Literacy in Food Businesses

AI has the potential to improve food safety training and communication, but communication can be hindered by various forms of noise, including channel limitations, time pressure, and message complexity. Food businesses that are deploying AI tools for internal food safety management, whether for HACCP documentation, supplier audit management, or staff training, need to invest in AI literacy as a food safety competency. Thus, training food safety teams to understand what AI systems can and cannot reliably do, to verify AI-generated regulatory information against primary sources, and to recognise the signs of hallucinated or outdated content is an upcoming requirement as it will safeguard the industry while expanding QA staff capabilities to adapt to new changes in the manufacturing sector. ISO 22000:2018's requirement for competence and awareness (Clause 7.2 and 7.3) provides an existing framework within which AI literacy can and should be situated.

 

Industry-Regulator Collaboration on Validation Standards

The FAO report identifies three core areas of AI deployment in food safety: a) scientific advice, b) inspection and border control, and c) operational activities of food safety competent authorities. For each of these domains, validation standards analogous to method validation requirements for laboratory testing need to be developed. An AI system making predictions about import violation probability should be evaluated against documented accuracy metrics, tested across diverse shipment types and origins, and revalidated when the model or its training data changes. These validation requirements do not yet exist in a standardised form, and developing them is a concrete, achievable step that the food safety community can take in the near term.

 

The Unanswered Questions

It is important to be honest about what we do not yet know, because the honest answer to several critical questions is that nobody knows yet, and that uncertainty itself should inform how cautiously we proceed.

 

We do not know, at a population level, how frequently AI-generated food safety advice is wrong, or how frequently those errors have health consequences. The surveillance infrastructure to detect AI-related food safety misinformation at a population level simply does not exist. We do not know what the threshold of public trust in AI food safety advice is, at what point a pattern of notable errors would cause consumers to discount AI guidance in ways that might themselves create safety risks (for instance, by causing people to distrust correct AI advice about a genuine food safety hazard). We do not know whether the regulatory frameworks being developed by the EU, FDA, and others will evolve quickly enough to address the rate at which AI capabilities and deployments are changing.

 

The 2025 FAO and Wageningen report notes that the risk of prematurely using AI in food safety, whether by applying techniques that are not yet suitable for the specific data or problem, or by implementing AI without the necessary expertise to interpret its output, lies in potentially undermining the trust and credibility of the organisation employing it, which is well-stated. But it frames the risk at the organisational level. The deeper risk, as AI-generated food safety advice reaches consumers directly through voice assistants and AI summary features in search engines, is that it undermines public trust in food safety guidance more broadly, including the authoritative guidance that comes from regulatory bodies and certified professionals who have earned that trust over decades.

 

Conclusion

Artificial intelligence is not going to stop being applied to food safety, and nor should it. The genuine benefits, in pathogen detection speed, improve surveillance efficiency, supply chain traceability, and predictive risk modelling are real, documented, and important. The food safety community's task is not to resist AI adoption, but to shape it to demand well-designed systems, appropriate governance, embedded human expertise, and honest communication about the limits of what AI can reliably know.

 

The analogy that feels most apt is the introduction of rapid microbiological testing methods into food safety laboratories in the 1990s and 2000s. Those methods were faster, cheaper, and more scalable than traditional culture methods, and they required new validation standards, new competency requirements, and new quality assurance frameworks before they could be trusted. The industry built those frameworks, and rapid methods are now a cornerstone of modern food safety. AI can follow a similar trajectory, but only if the governance work keeps pace with the technology deployment, and in the current context, it is not.

 

Food safety professionals should be curious about AI, and should use it where it has been validated, verified its outputs against primary sources, and should invest in the AI literacy of their food safety teams. The regulators should realize that the window for proactive governance is narrowing, while consumers should treat AI food safety advice as a starting point for a question, not an endpoint for a decision. Thus, the question is not whether to trust AI in food safety, but rather how to build the conditions under which that trust is justified.

 

 

References

[1] van Meer, F., van der Velden, B. & Takeuchi, M. 2025. Artificial Intelligence for Food Safety – A Literature Synthesis, Real-World Applications and Regulatory Frameworks. FAO & Wageningen Food Safety Research. https://www.fao.org/food-safety/news/news-details/en/c/1748997/

[2] New Food Magazine. (2025, July 30). AI in food safety: real-world solutions from IAFP 2025. https://www.newfoodmagazine.com/news/253921/ai-in-food-safety-iafp-2025/

[3] International AI Safety Report 2026. International Scientific Report on the Safety of Advanced AI. https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026

[4] Food Safety Magazine. (2025, October 31). FAO Report Highlights Needs for Responsible AI Adoption in Food Safety Fields. https://www.food-safety.com/articles/10845-fao-report-highlights-needs-for-responsible-ai-adoption-in-food-safety-fields

[5] ScienceDirect. (2025, April). Advancing food safety behavior with AI: Innovations and opportunities in the food manufacturing sector. https://www.sciencedirect.com/science/article/pii/S0924224425001864

[6] Academia.edu / Open Access. (2025). Artificial intelligence in food safety and nutrition practices: opportunities and risks. https://www.academia.edu/3067-1345/2/3/10.20935/AcadNutr7904

[7] ScienceDirect. (2025, September). Food safety – the transition to artificial intelligence (AI) modus operandi. https://www.sciencedirect.com/science/article/pii/S0924224425004145

[8] ScienceDirect. (2025, September). Harnessing Artificial Intelligence to Safeguard Food Quality and Safety. https://www.sciencedirect.com/science/article/pii/S0362028X25001735

[9] Food Safety Magazine. (2026, March). Leveraging AI for Food Safety Without Becoming Its Victim. https://www.food-safety.com/articles/11280-leveraging-ai-for-food-safety-without-becoming-its-victim

[10] Exploration Publishing. (2025, October). The role of artificial intelligence (AI) in foodborne disease prevention and management—a mini literature review. https://www.explorationpub.com/Journals/edht/Article/101167

 

Microplastics in Food and Water

Are We Eating Plastic? The Hidden Contamination Crisis from Soil to Table

Microplastics (MPs) — defined as plastic particles with at least one dimension below 5 millimetres — have become one of the most pervasive emerging contaminants in global food and water systems. Unlike conventional food safety hazards, MPs do not originate from a single industrial process, pathogen, or chemical group; they are a structural consequence of the modern plastic economy itself. Global plastic production exceeded 400 million tonnes per year by the early 2020s, and less than 10% is effectively recycled, with the remainder fragmenting through UV exposure, mechanical weathering, and biological activity into particles that migrate into virtually every compartment of the human food chain. Primary polymers detected in food systems include polyethylene terephthalate (PET), polypropylene (PP), and polyethylene (PE), principally resulting from the degradation of bottles, bottling processes, and contaminated source water [1]. The article examines the hazard profile of MPs as a food safety concern, traces the contamination pathways from agricultural soil to finished food products, and evaluates current and emerging control strategies within food safety management frameworks.

 

Prevalence in the Food Supply: Quantified Contamination Across Food Categories

The ubiquity of MP contamination across food matrices is now well established across more than a decade of global monitoring research. MPs have been detected in a wide range of food sources, including vegetables (6.4 particles/100 g), honey (1,992–9,752 particles/kg), sugar (249 ± 130 particles/kg), cereals (5.7 particles/100 g), dairy products (8.1 particles/100 g), meats (9.6 particles/100 g), beers (152 ± 50.97 particles/L), and soft drinks (40 ± 24.53 particles/L) [2]. These figures reveal that contamination is not confined to any single food category or processing pathway; it is systemic across both animal and plant-derived products, processed and raw, regardless of geographic origin.

 

Seafood has received the greatest research attention, given the expectation that filter-feeding marine organisms would accumulate elevated MP loads. Shellfish and other animals consumed whole pose particular concern for human exposure, and if there is toxicity it is likely dependent on dose, polymer type, size, surface chemistry, and hydrophobicity [3]. Bivalves such as oysters, mussels, and clams have consistently returned among the highest contamination levels in studies across Europe, Asia-Pacific, and North America. In the case of drinking water, both tap and bottled sources are implicated. MPs abundance in bottled water has been reported as higher than that in tap water, and plastic particles can enter bottled water during the process of bottle cleaning, filling, and capping, with cap abrasion identified as the main entry path into bottled mineral water [4]. This is a critical finding for food safety professionals: switching to bottled water as a mitigation strategy for other contaminants may inadvertently increase MP exposure. Relying on bottled water for drinking needs can increase the amount of microplastics ingested by more than six times compared to tap water consumption [5].

 

The scale of human dietary intake is substantial. Scientific estimates suggest that humans ingest on the order of a credit card's worth of plastic per week across food, water, and inhalation routes combined, and people in the U.S. could be ingesting 4,000 microplastic particles or more through tap water each year [5] The detection of MPs in human blood, placental tissue, gastrointestinal samples, and lung tissue confirms that dietary and inhalation exposure translates into systemic bioaccumulation, not merely gastrointestinal transit.

 

Microplastics in Agriculture: Soil-to-Food Transfer

While aquatic contamination pathways have been extensively studied, the terrestrial food chain — and in particular the agricultural soil-to-crop transfer pathway — represents a less-characterised but equally significant contamination route. With increasing amounts of microplastics deposited in soil from various agricultural activities, crop plants have become an important source of MPs in food products, and the last three years of studies have provided sufficient evidence showing that plastic in the form of nanoparticles can be taken up by the root system and transferred to aboveground plant parts [6], which is a development of fundamental significance to food safety hazard analysis: it means that even leafy vegetables, cereals, and root crops grown without any direct plastic contact in processing can carry MPs acquired during cultivation.

 

Agricultural soils accumulate MPs from multiple entry routes. Plastic mulch films — widely used for weed suppression and soil moisture retention across China, Southern Europe, the Middle East, and increasingly in North America — fragment progressively in the field. In agriculture, MPs can come from many sources including mulch film, tractor tires, compost, fertilizers, and pesticides, and can alter soil structure and composition, triggering a cascade of changes in the biophysical environment of the soil [7]. Sewage sludge application for soil nutrient management represents a particularly significant source: biosolids applied as fertiliser in many agricultural systems carry MP loads accumulated during wastewater treatment, where MPs are removed from the aqueous fraction and concentrated into the solid residual. Irrigation with treated wastewater introduces further loads, as does atmospheric deposition from industrial and urban sources.

 

Estimates suggest that approximately 32% of global plastic waste may end up in terrestrial ecosystems, with a substantial fraction accumulating in agricultural lands, [8] making soils the largest environmental reservoir of MPs globally, exceeding ocean surface concentrations by orders of magnitude on a per-unit-volume basis. Once in soil, MPs interact with the agricultural system in ways that extend well beyond their role as physical contaminants. Heavier amounts of microplastics have been linked to slower root development, changes in how nutrients cycle through the soil, and altered interactions with other contaminants like pesticides or heavy metals, and microplastic surfaces provide a home for bacteria and fungi, sometimes called the "plastisphere" – which in some cases can include harmful organisms that pose risks to crops or human health [9]. The "plastisphere" concept is particularly relevant to food safety hazard identification: MPs in soil and water function not merely as inert physical particles but as mobile vectors for microbial hazards and adsorbed chemical contaminants, concentrating pesticides and heavy metals at their surfaces and carrying them through the soil-plant continuum.

 

Hazard Profile: Physical, Chemical, and Biological Dimensions

The hazard profile of MPs in food is multidimensional, and this complexity is one of the primary reasons that risk characterisation has lagged behind hazard identification. Three distinct hazard mechanisms are relevant to food safety analysis.

 

Physical hazard 

Particle size determines the depth of biological penetration. Larger MPs (1–5 mm) are primarily retained in the gastrointestinal tract and excreted. Particles below 150 micrometres are capable of crossing the intestinal epithelium, and nanoplastics (below 1 micrometre) can translocate across the gut barrier into the bloodstream and accumulate in systemic tissues. MPs have been detected in human blood, placental tissue, and gastrointestinal samples, indicating systemic exposure, with proposed biological pathways including oxidative stress, inflammation, endocrine disruption, and alterations in the gut microbiota [10]. The mechanical abrasion of tissue surfaces by irregularly shaped MP fragments, and the potential for persistent particle accumulation in organs including the liver, kidney, and lymphatic tissue, constitutes a physical hazard distinct from any chemical toxicity.

 

Chemical hazard 

Plastics are not chemically inert. They contain a range of intentionally added chemical substances; including plasticisers, stabilisers, flame retardants, pigments, and antioxidants that may leach from the polymer matrix into food matrices or biological systems. Polyvinyl chloride contains phthalates associated with endocrine disruption, polycarbonate often includes bisphenol A linked to reproductive disorders, styrene (a component of polystyrene) is classified as a probable human carcinogen, and polyethylene terephthalate may release toxic antimony compounds under high temperatures [11]. In addition to intentionally added substances, MPs adsorb persistent organic pollutants, heavy metals, and pesticide residues from the surrounding environment, concentrating them at particle surfaces and potentially delivering them to biological tissues upon ingestion — a mechanism described in the literature as the "Trojan horse" effect.

 

Biological hazard 

The plastisphere; the microbial biofilm community that colonises MP surfaces can selectively enrich pathogenic bacterial taxa and antimicrobial resistance genes. Studies have demonstrated that MP biofilms in aquatic and soil environments support elevated concentrations of E. coli, Vibrio species, and antibiotic-resistant organisms relative to surrounding water or soil. The findings introduce a novel dimension to microbial food safety risk that is not captured by conventional pathogen monitoring programs.

 

Root Cause Analysis: Why Contamination is Systemic

A root cause analysis of MP food contamination reveals that it is not an isolated processing failure but the product of structural choices embedded across the food system. At the primary production level, the intensive use of plastic in agriculture such as mulch films covering an estimated 20–30 million hectares globally, plastic-coated fertilisers, plastic irrigation infrastructure, and plastic greenhouse films; that introduces MPs directly into the production environment without any equivalent removal step. At the processing and packaging level, plastic contact surfaces, packaging materials, and bottling operations generate MPs through abrasion and degradation. At the retail and domestic level, plastic food containers, cooking utensils, single-use packaging, and synthetic textiles in the laundry stream contribute further loads to the broader environment that re-enter the food chain through water systems.

 

A further structural root cause is the absence of standardised analytical methods. Concentrations fluctuate significantly across studies, ranging from a handful to several hundred particles per litre, influenced by the type of beverage, packaging material, and method of analysis employed; the lack of standardised methods hinders comparability of data [1]. Such methodological fragmentation has delayed the development of regulatory limits and risk-based management thresholds. Unlike mycotoxins or heavy metals, for which internationally harmonised analytical methods (e.g., AOAC, ISO) and maximum limits (e.g., Codex Alimentarius, EU Regulation) exist, MPs currently have no equivalent framework in any major food regulatory jurisdiction. The FDA has noted that because there are no standardised methods for how to detect, quantify, or characterise microplastics and nano-plastics, many of the scientific studies have used methods of variable, questionable, and/or limited accuracy and specificity [12].

 

Health Effects: Emerging Evidence and Unresolved Uncertainties

The health implications of chronic dietary MP exposure are an area of intense and rapidly evolving research. Evidence from animal models and in vitro studies is substantial; however, direct epidemiological evidence linking MP exposure to specific human disease outcomes remains limited due to the absence of long-term cohort studies with validated biomarkers of MP exposure.

 

At the gastrointestinal level, MP exposure has been mechanistically linked to gut dysbiosis, a condition of impaired microbial diversity and altered functional capacity. Exposure to MPs such as polyethylene, polystyrene, PET, PVC, and polylactic acid induces gut dysbiosis, marked by a loss of beneficial genera and enrichment of pathogenic species [13]. MP-induced dysbiosis further disrupts intestinal barrier integrity, contributing to increased permeability — sometimes described as "leaky gut" — which is associated with systemic inflammatory conditions. The inflammatory response induced by MP exposure may also affect the gut-brain axis, a complex bidirectional communication network between the GI system and the central nervous system, where dysbiosis can disrupt these functions, leading to neuroinflammation, which is implicated in neurological and psychiatric conditions such as anxiety, depression, and cognitive decline [14].

 

Endocrine disruption is among the most concerning potential health effects, particularly for reproductive health and developmental outcomes. Chemical additives including BPA, phthalates, and organotins associated with common polymer types are established endocrine-disrupting chemicals at environmentally relevant exposure levels. Evidence suggests that MNP exposure might elevate the risk of various diseases, including metabolic, respiratory, cardiovascular, neuroendocrine, hepatic, renal, and skin disorders, as well as infectious diseases, cancer, and ageing-related disorders [11]. With respect to carcinogenicity, microplastics are capable of triggering cytotoxicity and chronic inflammation, and may promote cancer through mechanisms such as pro-inflammatory responses, oxidative stress, and endocrine disruption, with current studies suggesting an association between microplastics and certain cancers including lung, liver, and breast cancers, although long-term effects and specific mechanisms still require further study [15].

 

It is important, however, to acknowledge the epistemic boundaries of the current evidence base. The FDA's current position reflects such uncertainty: current scientific evidence does not demonstrate that the levels of microplastics or nanoplastics detected in foods pose a risk to human health [12]. This regulatory position is not a declaration of safety — it reflects the absence of sufficient dose-response data to establish causality at human exposure levels. The precautionary principle, as applied under ISO 22000:2018 and Codex Alimentarius principles, would warrant hazard identification and monitoring even in the absence of confirmed risk quantification.

 

Control Strategies: From Farm to Consumer

Given that MP contamination is systemic and multi-source, no single control point within a conventional HACCP framework can be designated as a Critical Control Point sufficient to eliminate or adequately reduce the hazard, which represents a fundamental challenge for food safety management system design and demands a preventive, whole-chain approach.

 

At the agricultural level, reducing plastic inputs is the highest-leverage intervention. The FAO's Voluntary Code of Conduct for the Sustainable Use and Management of Plastics in Agriculture (2025) represents the first international guidance framework specifically addressing agricultural plastic management, recommending minimum thickness standards for mulch films to extend functional life and reduce fragmentation rates, and promoting the development and commercialisation of certified biodegradable alternatives, where lifecycle assessments demonstrate net environmental benefit. However, biodegradable plastics are not without risk: studies have identified that some biodegradable polymer types release potentially phytotoxic breakdown products during degradation, and "biodegradable" does not necessarily mean rapid or complete soil mineralisation under field conditions.

 

At the water treatment level, conventional wastewater treatment removes a significant proportion of MPs from the aqueous fraction, where estimates range from 70% to over 99% removal efficiency, but that concentrates the removed particles in sewage sludge. Where that sludge is applied to agricultural land, the removal achieved in wastewater treatment is effectively reversed at the field level. Advanced filtration technologies including membrane bioreactors, rapid sand filtration, and coagulation-flocculation processes can achieve higher removal rates from drinking water, but their implementation in municipal systems is inconsistent globally. Efforts to reduce impact should prioritise sustainable packaging materials, sophisticated filtration systems, regulatory standards, and consumer education to decrease exposure [1].

 

At the food production and processing level, ISO 22000:2018 provides a relevant framework for addressing MPs as an environmental contaminant through the Prerequisite Programme (PRP) and Operational PRP structure. Relevant PRPs include: facility design and construction (selection of non-shedding food-contact surface materials, elimination of polystyrene and PVC in food-contact applications), equipment maintenance (monitoring of wear rates on plastic components including conveyor belts, gaskets, and tubing), water quality management (MP-specific monitoring in process water), and packaging material selection (preference for glass or certified low-migration polymers for high-risk applications). Within the hazard analysis, MPs should be evaluated as a physical hazard under the hazard identification step, with particular attention to packaging-derived contamination in high-temperature processing applications where polymer degradation and chemical migration rates increase significantly.

 

Detection technologies for MPs in food systems have advanced considerably in recent years, moving from labour-intensive microscopic methods toward spectroscopic and hyperspectral imaging platforms. Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy provide polymer identification at the particle level, while pyrolysis-gas chromatography mass spectrometry (Py-GC-MS) enables mass-based quantification of polymer types in complex matrices. Emerging artificial intelligence-assisted imaging platforms are beginning to enable higher-throughput screening, though their validation for regulatory applications remains at an early stage.

 

Regulatory Status and Governance Gaps

The regulatory governance of MPs in food and water is currently fragmented and inadequate relative to the scale of the contamination. No Codex Alimentarius maximum limit exists for MPs in any food category. In the European Union, the Single-Use Plastics Directive (2019/904) and the proposed Packaging and Packaging Waste Regulation represent structural measures targeting plastic reduction upstream, but do not establish food safety limits for MP contamination of finished products. The REACH regulation's restriction on the intentional addition of microplastics to products (Commission Regulation (EU) 2023/2055) addresses a significant source of primary MPs from personal care products, fertiliser coatings, and sports surfaces, but does not address the much larger reservoir of secondary MPs generated by weathering of existing plastic infrastructure.

 

In the United States, seven State Governors petitioned the EPA in late 2025 for mandatory monitoring requirements for MPs in public drinking water systems, reflecting the absence of any current federal monitoring requirement. The EPA's framework for interagency collaboration on antimicrobial resistance risks associated with pesticides (2024) signals growing regulatory attention to the intersection of agricultural chemical use and broader public health outcomes, though MPs are not yet formally addressed within this framework.

 

Conclusion

Microplastics represent a novel, pervasive, and complex food safety challenge that sits outside the traditional paradigm of point-source contamination. Their presence in virtually every food category, their multi-pathway entry into the food chain from soil, water, packaging, and processing, and their multidimensional hazard profile — physical, chemical, and biological — make them a priority concern for food safety management systems, regulatory science, and public health research alike. The critical knowledge gaps remain dose-response characterisation at human-relevant exposure levels, standardisation of analytical methods to enable regulatory limit-setting, and understanding of long-term health consequences through prospective epidemiological studies. For food safety professionals operating under ISO 22000:2018 or FSSC 22000, the current evidence base is sufficient to warrant formal hazard identification and documentation within hazard analysis, and the precautionary principle supports proactive operational measures to reduce MP contamination at every stage of the supply chain where technically feasible.

 

 

References

[1] Prevalence and health risks of microplastics in bottled water and beverages. ScienceDirect. 2025. https://www.sciencedirect.com/science/article/pii/S3051060025000241

[2] Yarahmadi A, Heidari M, Sepahvand A, et al. Microplastics and environmental effects: investigating the effects of microplastics on aquatic habitats and their impact on human health. Frontiers in Ecology and Evolution. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11191580/

[3] Smith M, Love DC, Rochman CM, Neff RA. Microplastics in Seafood and the Implications for Human Health. Curr Environ Health Rep. 2018 Sep;5(3):375-386. doi: 10.1007/s40572-018-0206-z. PMID: 30116998; PMCID: PMC6132564.

[4] Duda A, Petka K. The presence of micro- and nanoplastics in food and the estimation of the amount consumed depending on dietary patterns. Molecules. 2025;30:3666. https://pmc.ncbi.nlm.nih.gov/articles/PMC12472390/

[5] Erin D., Natalie B., The Mega-Crisis of Microplastics in Our Drinking Water, , https://www.foodandwaterwatch.org/2024/11/25/microplastics-drinking-water-petition/

[6] Brzezicha-Cirocka J, et al. Microplastic and nanoplastic in crops: possible adverse effects to crop production and contaminant transfer in the food chain. PMC. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11397527/

[7] Chen Y, et al. Microplastics in agricultural crops and their possible impact on farmers' health: a review. PMC. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC11765068/

[8] Chaudhary, H.D., Shah, G., Bhatt, U. et al. Microplastics and plant health: a comprehensive review of sources, distribution, toxicity, and remediation. npj Emerg. Contam. 1, 8 (2025). https://doi.org/10.1038/s44454-025-00007-z

[9] Penn State Extension. Microplastics in agricultural lands. 2025. https://extension.psu.edu/microplastics-in-agricultural-lands

[10] Ririe C, et al. Impact of microplastic exposure on human health: a systematic review of mechanisms, biomarkers, and clinical outcomes. PMC. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12848325/

[11] Ali N, Katsouli J, Auyang E et al., Microplastic and nanoplastic pollution and associated potential disease risks, The Lancet Planetary Health, 2025; 9 https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(25)00268-2/fulltext

[12] FDA. Microplastics and nanoplastics in foods. U.S. Food and Drug Administration. https://www.fda.gov/food/environmental-contaminants-food/microplastics-and-nanoplastics-foods

[13] Thin ZS, et al. Impact of microplastics on the human gut microbiome: a systematic review of microbial composition, diversity, and metabolic disruptions. BMC Gastroenterology. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12351775/

[14] Bora S, et al. Microplastics and human health: unveiling the gut microbiome disruption and chronic disease risks. Frontiers in Cellular and Infection Microbiology. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11635378/

[15] Gao Y, et al. The micro(nano)plastics perspective: exploring cancer development and therapy. PMC. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC11761189/

Additional References

1. Steer M, et al. Exposure to microplastics from food: comparative analysis of food types and quantification techniques. Science of the Total Environment. 2025. https://www.sciencedirect.com/science/article/pii/S0304389425035770

2. Zhang X, et al. Microplastics and human health: unraveling the toxicological pathways and implications for public health. Frontiers in Public Health. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12213550/

3. Suwannee E, et al. Mind over microplastics: exploring microplastic-induced gut disruption and gut-brain-axis consequences. PMC. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11120006/

4. Landrigan PJ, et al. Microplastic and nanoplastic pollution and associated potential disease risks. The Lancet Planetary Health. 2025. https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(25)00268-2/fulltext

5. Arias AH, et al. Microplastics and nanoplastics: fate, transport, and governance from agricultural soil to food webs and humans. Environmental Sciences Europe. 2025. https://link.springer.com/article/10.1186/s12302-025-01104-x